Deep learning for image segmentation: veritable or overhyped?
This work critically assesses deep learning's effectiveness for image segmentation, highlighting potential overhyping and verification issues, which is important for researchers and practitioners in computer vision and medical imaging.
This paper surveys deep learning's performance in image segmentation, finding that while it achieves high accuracy (around 95%) in biomedical challenges, it only reaches about 80% in general vision challenges, and a threshold selection method outperforms deep learning methods in segmentation accuracy.
Deep learning has achieved great success as a powerful classification tool and also made great progress in sematic segmentation. As a result, many researchers also believe that deep learning is the most powerful tool for pixel level image segmentation. Could deep learning achieve the same pixel level accuracy as traditional image segmentation techniques by mapping the features of the object into a non-linear function? This paper gives a short survey of the accuracies achieved by deep learning so far in image classification and image segmentation. Compared to the high accuracies achieved by deep learning in classifying limited categories in international vision challenges, the image segmentation accuracies achieved by deep learning in the same challenges are only about eighty percent. On the contrary, the image segmentation accuracies achieved in international biomedical challenges are close to ninty five percent. Why the difference is so big? Since the accuracies of the competitors methods are only evaluated based on their submitted results instead of reproducing the results by submitting the source codes or the software, are the achieved accuracies verifiable or overhyped? We are going to find it out by analyzing the working principle of deep learning. Finally, we compared the accuracies of state of the art deep learning methods with a threshold selection method quantitatively. Experimental results showed that the threshold selection method could achieve significantly higher accuracy than deep learning methods in image segmentation.